Invest Like the Best with Patrick O'Shaughnessy
· Jesse Zhang, Patrick O'Shaughnessy
And I think this just goes to show that as a founder, you kind of have to build your own conviction and kind of do this process yourself. Because at the time, essentially what everyone told us, including very smart older founders that we knew and so on, was that excuse case super obvious. There's probably just going to be incumbents that are just tacking onto the product. And because it's so obvious, there's probably a reason why no one's super big right now or there's going to be someone that's ahead, but no one really knows. And even for me right now, and other founders talk to me about other spaces, it's like, yeah, I have my own opinions, but I don't really know the details of the space. And the only way you can really know is by talking to customers and getting that signal. And in hindsight it turns out that in any sort of wave at any time, I would say a very small number of good ideas. And your job is to ideally find one of those at the right time. And so, yeah, by the definition it's going to be pretty non obvious and pretty difficult. And so if you can do this process well, it'll give you the most signal. [00:00:00] S0: Do you remember the highest number anyone said for how much they'd be willing to pay for something in one of these ideation sessions? [00:00:00] S1: Yeah. So the time was probably on the order of low to mid six figures.
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: Yeah. So the time was probably on the order of low to mid six figures. [00:00:00] S0: As you neared the end of that process and settled on what Decagon does, which maybe probably is the right time to ask you to describe in detail what it is. What was like the final closing, like, where did the conviction come from? Like, oh, this is clearly the thing. After this discovery process.
· Jesse Zhang, Patrick O'Shaughnessy
[00:00:00] S1: It was clearly the thing because if you just kind of tallied up even just the amounts that people said and added them together per idea, this was probably like an order of magnitude more than anything else. So very specifically what Decagon does, it's an AI customer service agent. So that's the simplest way to think about it. And you're building a conversational AI that can just be almost like a front end for a brand. Anytime someone wants to talk to the brand, or anytime the brand wants to talk to them, you can kind of initiate these conversations. And of course, long term, this is not specific to customer service, but I think again, going back to this exercise, customer service is where we felt like the most urgent need. And in hindsight I can dissect why we think that is, but that's basically what we felt and what gave us conviction is that, hey, we had all these folks that were lined up, they were like very willing to invest six figures in a random two person team. They didn't even know that well because it was actually like a very top of mind initiative for them. Everything else, it was just like a struggle to. It's like how much we pay for that, I don't know. This is really exciting, but our budgets are tight right now and also it'd be hard to measure how well this is doing and so on. So yeah, that gave us enough conviction and then you just kind of take it step by step from there. [00:00:00] S0: Say more about the comment you made about. In hindsight, it's clear why this was the key problem.
· Jesse Zhang
[00:00:00] S1: Yeah. So I would say customer service has a bunch of nice properties that I think are very hard to reason through ahead of time. Which is why I think I feel so strongly about this process of discovery. Just really staying customer centric. One of the properties is that the ROI is really easy to justify. Internally you have these numbers already tracked. It's like, hey, we have so much conversation volume right now. We have a simple chatbot or a simple IVR phone tree. It's resolving, so to speak, like 15 to 20% of that. If you're able to take that to 50, 60, 70, 80, like that's huge ROI. And it's very easy to quantify. It's like, okay, well I'm going to take the total cost, I'm going to chop off 60% of it and that's what I'm saving. The other property, which I think is a little underrated, is that it's very easy to go live. I think a lot of Gen AI use cases are struggling with that right now, especially at the enterprise level, because there's risk involved. People don't want to feel like something could go wrong for them. When they release your product, leadership's going to get mad at them. It's like, you know, why'd you do this? So that is a big deal with Gen AI. I think that is one of the reasons why there's been difficult for a lot of use cases to really take off because at the end of the day there is always going to be risk because the models are non-deterministic and so something could always happen. But the nice thing with customer service is that you have a escalation path just naturally built into the way the product works. The agent's having the conversation. If for any reason it needs to exit, it'll just escalate to a human. And that infrastructure is already set up. You already have your call center, you already have your telephony stack or whatever, so you just connect to it. I think that property alone just makes things way easier because these big enterprises that we work with, they're like, okay, great, we tested it internally and then to go live, we're just going to choose this one surface area and release it to 5% of the user base. And even for that 5%, if anything goes wrong, it just escalates. And so that gives people enough comfort to go for it. Those two things I think are one of the big reasons why it's probably the, I would argue the use case with the most traction at the enterprise. And coding is another one.
· Jesse Zhang, Patrick O'Shaughnessy
Coding is a little different. It's a lot more bottoms up. [00:00:00] S0: Can you compare it to coding? It seems like these are the two areas where it's blindingly obvious that it's useful to customers and you can build great businesses around it. Just looking at the revenue curves, your peers are obviously like Cursor, Cognition, et cetera. Compare and contrast. Coding and customer service.
· Jesse Zhang
[00:00:00] S1: Yeah, they're very different. Maybe one framework to think about this is that at the end of the day, what AI agents are there for is to essentially replace human labor. That's why it's exciting, that's why everyone's so focused on it. One thing you can do then is you can just map out the spectrum of how much that human labor currently costs. So with customer service, it's generally outsourced already, especially for the tier one, tier two type inquiries that AI is now handling. It's generally not folks that are super highly paid, and then on the other end, it's engineers, which is the most highly paid people. The one way to think about it is that AI use cases will start eating the spectrum from both ends. And the reason why is that because engineers are the highest paid, they have the sophistication to really leverage it well. And AI just gives them so much leverage. I mean, there's other factors as well. It just happens that coding is tokenizable and the models are really good at it. That's one way to think about it. I don't know any company that's saying, hey, I would like to let go of a bunch of my engineers because now I have coding agents. There's infinite engineering work to do. So you're just augmenting them. On the other end, it is more of the replacement sense. You have a large BPO and that's costing you a ton of money. And it's also a really high operational thing to maintain because you have to hire people all the time. There's a ton of churn. You need to train them, you need to QA them, you need to make sure that nothing goes wrong. So AI is really valuable there as well, because the work is easier for the AI to do. It can fully replace. And I think that goes into the conversation of, I think it's a little bit overblown of AI is replacing jobs and so on. Even the BPOs we talk to, they're not really that concerned because what typically happens anyways is that there's already very high turnover in these BPOs. People are just hopping around, doing all sorts of different things, kind of just naturally let it decrease, and then they just go on to do sort of the next level of task that the AI can't do yet. Right. And so maybe that's data labeling or something. That's generally what we're seeing from the BPOs. But yeah, anyways, I think this spectrum is pretty real. And so then the question is what is the next thing that happens?